{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "运行环境：jupyter notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data=pd.read_csv('card_transdata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000000, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Max-Min标准化\n",
    "\n",
    "from sklearn import preprocessing \n",
    "minmax_scaler=preprocessing.MinMaxScaler()\n",
    "data_minmax_1=minmax_scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = data_minmax_1.shape[1]\n",
    "X = data_minmax_1[:, 0:cols - 1]\n",
    "y = data_minmax_1[:, cols - 1:cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000000, 7)\n",
      "(1000000, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#划分固定的训练集和测试集\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.01,random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# （1）随机过采样\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "ros = RandomOverSampler(random_state=0)\n",
    "X_oversampled, y_oversampled = ros.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (2)SMOTE过采样：\n",
    "from imblearn.over_sampling import SMOTE\n",
    "smote = SMOTE(random_state=0)\n",
    "X_smotesampled, y_smotesampled = smote.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(990000, 7)\n",
      "(990000, 1)\n",
      "(10000, 7)\n",
      "(10000, 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们用这个固定的训练集去训练模型\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "\n",
    "#测试集检验模型泛化能力\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "y_oversampled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
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       "      <th>distance_from_last_transaction</th>\n",
       "      <th>ratio_to_median_purchase_price</th>\n",
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      ],
      "text/plain": [
       "        distance_from_home  distance_from_last_transaction  \\\n",
       "366064            2.201878                        1.085001   \n",
       "392048           12.779981                        2.019137   \n",
       "22429            71.530724                        0.247719   \n",
       "529204          500.961637                        1.282759   \n",
       "471991            6.008008                        0.259639   \n",
       "...                    ...                             ...   \n",
       "192476           18.375816                       41.775537   \n",
       "17730            12.567937                        4.659560   \n",
       "28030            49.165689                        0.051509   \n",
       "277869           15.103890                        1.414150   \n",
       "773630            5.268070                        7.196594   \n",
       "\n",
       "        ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "366064                        0.161537              1.0        0.0   \n",
       "392048                        2.713632              1.0        1.0   \n",
       "22429                         0.335093              1.0        0.0   \n",
       "529204                        2.527719              1.0        1.0   \n",
       "471991                        0.399418              1.0        0.0   \n",
       "...                                ...              ...        ...   \n",
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       "17730                         2.226731              1.0        0.0   \n",
       "28030                         4.576448              1.0        0.0   \n",
       "277869                        0.301148              1.0        0.0   \n",
       "773630                        0.836012              1.0        0.0   \n",
       "\n",
       "        used_pin_number  online_order  \n",
       "366064              0.0           0.0  \n",
       "392048              0.0           1.0  \n",
       "22429               0.0           1.0  \n",
       "529204              0.0           1.0  \n",
       "471991              0.0           0.0  \n",
       "...                 ...           ...  \n",
       "192476              1.0           1.0  \n",
       "17730               0.0           0.0  \n",
       "28030               1.0           1.0  \n",
       "277869              0.0           0.0  \n",
       "773630              0.0           1.0  \n",
       "\n",
       "[990000 rows x 7 columns]"
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train=X_train\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <th>ratio_to_median_purchase_price</th>\n",
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      ],
      "text/plain": [
       "        distance_from_home  distance_from_last_transaction  \\\n",
       "366064            2.201878                        1.085001   \n",
       "392048           12.779981                        2.019137   \n",
       "22429            71.530724                        0.247719   \n",
       "529204          500.961637                        1.282759   \n",
       "471991            6.008008                        0.259639   \n",
       "...                    ...                             ...   \n",
       "192476           18.375816                       41.775537   \n",
       "17730            12.567937                        4.659560   \n",
       "28030            49.165689                        0.051509   \n",
       "277869           15.103890                        1.414150   \n",
       "773630            5.268070                        7.196594   \n",
       "\n",
       "        ratio_to_median_purchase_price  repeat_retailer  used_chip  \\\n",
       "366064                        0.161537              1.0        0.0   \n",
       "392048                        2.713632              1.0        1.0   \n",
       "22429                         0.335093              1.0        0.0   \n",
       "529204                        2.527719              1.0        1.0   \n",
       "471991                        0.399418              1.0        0.0   \n",
       "...                                ...              ...        ...   \n",
       "192476                        0.222252              1.0        0.0   \n",
       "17730                         2.226731              1.0        0.0   \n",
       "28030                         4.576448              1.0        0.0   \n",
       "277869                        0.301148              1.0        0.0   \n",
       "773630                        0.836012              1.0        0.0   \n",
       "\n",
       "        used_pin_number  online_order  fraud  \n",
       "366064              0.0           0.0    0.0  \n",
       "392048              0.0           1.0    0.0  \n",
       "22429               0.0           1.0    0.0  \n",
       "529204              0.0           1.0    0.0  \n",
       "471991              0.0           0.0    0.0  \n",
       "...                 ...           ...    ...  \n",
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       "17730               0.0           0.0    0.0  \n",
       "28030               1.0           1.0    0.0  \n",
       "277869              0.0           0.0    0.0  \n",
       "773630              0.0           1.0    0.0  \n",
       "\n",
       "[990000 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['fraud'] = y_train\n",
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x22385f5b640>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1312x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2  hue：针对某一字段进行分类\n",
    "import seaborn as sns\n",
    "sns.pairplot(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x223927cc970>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "KNeighborsClassifier?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., ..., 0., 0., 0.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train_1=y_train.values.ravel()#转成一维的\n",
    "y_train_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time - 1670840075.4282303\n",
      "1\n",
      "time - 679.9396257400513\n",
      "0.9996646293695678\n",
      "time - 1670840755.367856\n",
      "3\n",
      "time - 737.1887021064758\n",
      "0.9994786812901533\n",
      "time - 1670841492.5565581\n",
      "5\n",
      "time - 794.4613182544708\n",
      "0.9993015878723632\n",
      "time - 1670842287.0178764\n",
      "7\n",
      "time - 811.8992338180542\n",
      "0.9991621268112709\n",
      "time - 1670843098.9171102\n",
      "9\n",
      "time - 812.5820081233978\n",
      "0.9990547639301527\n",
      "time - 1670843911.4991183\n",
      "11\n",
      "time - 836.709805727005\n",
      "0.9989518283882692\n",
      "time - 1670844748.208924\n",
      "13\n",
      "time - 836.972494840622\n",
      "0.9988654953489918\n",
      "time - 1670845585.181419\n",
      "15\n",
      "time - 877.6215527057648\n",
      "0.9987902306631611\n"
     ]
    },
    {
     "data": {
      "image/png": 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1IJdhvVuTpmldROKOXg5KTDAzhn6vIb+/qinvL9nCDx6fxoINu8KOJSLfkkpFYsrNHTN5cWB79uXmcc0/pjN62ioN4IvEEZWKxJxODary5tCudG2Uzu//vYh+z85i295DYccSkVOgUpGYVDktmZF92/L7q5oy/YvtdPv7VKYs2xp2LBE5CZWKxCwz4+aOmUy+83wqpZai7zMz+fN/FpOblx92NBEphEpFYt65Z5Vn8p2dubF9HUZMWcm1w6ezatu+sGOJyHGoVCQulElO5I9XN2f4TW1Ys30/3R+byqufrQ87logcQ6UicaVbsxq8ObQLTWtW4CcT5nH3+DnsOXg47FgiElCpSNypWbEM4wZ34J7vNWLyvI1c+dg05ur+LCIxQaUicSkxIXKx5ITbOnIk37n2qek89dEXmjtMJGQqFYlrWZmV+c+Pu/D9pmfx0FtL6PvMTLbsPhh2LJESS6Uica9CaimeuKE1D17TnJw1O+g2bCofLNkcdiyREkmlIsWCmdG7XR3+fVdnqpcvTf9nc/jd6ws5lHck7GgiJYpKRYqVs6uVY9KPOnFrp0zGfLKaHz45nRVb9oYdS6TEUKlIsVO6VCK/7dGU0bdksXn3QX7w+DRenrVWE1OKnAGnVSpmVraog4gUtUsaV+fNoV1oU7ciP584nztfmsOuA7qmRSSaTvdMZVGRphCJkurlSzO2f3t+3u1c3l74JVcMm8rsNTvCjiVSbBV6az0z+0lhqwCdqUjcSEgw7riwAR3qV+bH4+dw/Yhshl7SkCEXnU1igoUdT6RYOdGZyp+ASkC5Y77KnmQ7kZjUuk4l/vPjLnRvUYNH3l3GDSOz2bTrQNixRIoVK2zw0symA3e5++zjrFvn7rWjHS7asrKyPCcnJ+wYcoa5O69+toFf/2sByUkJPNSzBd9velbYsUTihpnNdves46070RnHBmCNmQ09zrrj7uw4v7ibmS01sxVmdv9x1lcys0lm9rmZzTSzZgXWDTWzBWa20MzuLrC8pZnNMLP5Zva6mZUPll9qZrOD5bPN7OJTySglj5nRs20Gb/y4C7UrpXLb2Nn86rX5HDysa1pEvqsTlUoTIA3oH/znX/noF3DSj9CYWSLwJHB5sK8+ZtbkmKc9AMx19xZAX2BYsG0zYBDQDmgJdDezhsE2o4D73b05MAn4WbB8G/CDYPktwNiTZZSSrV7VNCbe0YnBXevzQvZaejwxjaVf7gk7lkhcO1GpjADeAs4FZh/zdSrvGbUDVrj7SnfPBcYDVx3znCbA+wDuvgTINLPqQGMg2933u3se8DFwdbDNOcCU4PG7QM9g+znuvjFYvhAobWYpp5BTSrDkpAQeuKIxz/Vvx459h+nxxDTGzlita1pETlOhpeLuj7l7Y+AZd6/v7vUKfNU/hX3XAtYV+Hl9sKygecA1AGbWDqgLZAALgK5mVsXMUoErgKNjOAuAHsHj6wosL6gnMMfdDx27wswGm1mOmeVs3ap7nkvEBY3SeXNoFzrUr8Kv/7WQwWNns3NfbtixROLOST/F5e53nOa+j/dZzWNf/j0IVDKzucBdwBwgz90XAw8RORN5i0j55AXb9AeGmNlsIp9G+9q/fDNrGmx72/FCufvT7p7l7lnp6emnc1xSTKWXS2HMrefxqysb89HSLVw+bCozvtgediyRuBLNjwav5+tnERnAxoJPcPfd7t7P3VsRGVNJB1YF60a7ext37wrsAJYHy5e4+2Xu3hYYB3xxdH9mlkFknKWvu3+ByLeUkGAM7FKfST86n9TkRG4Ylc3f3llK3pH8sKOJxIVolsosoKGZ1TOzZKA3MLngE8ysYrAOYCAwxd13B+uqBd/rEHmLbNwxyxOAXwHDj+4LeAP4hbt/EsXjkhKgWa0KvH5XZ65tk8HjH6zg+hEzWLdjf9ixRGJe1EolGGC/E3gbWAxMcPeFZna7md0ePK0xsNDMlhD5lFjBjy9PNLNFwOvAEHffGSzvY2bLgCVEznzGBMvvBM4Gfm1mc4OvatE6Pin+0lKSePi6ljzWpzXLN+/lisem8vq8jSffUKQEK/Tix5JAFz/KqVq3Yz8/Hj+HOWu/4vqsDH7boympyYXOciRSrJ3uxY8iEqhdOZUJt3XkzovO5p+z19P98Wks2LAr7FgiMUelInKKSiUmcO/3z+HFge3ZdyiPa/4xndHTVumaFpECVCoi31KnBlV5c2hXujZK5/f/XsSNoz5l0cbdYccSiQkqFZHTUDktmZF92/LHq5uxaNNurnx8Kve9Mo/Nuw+GHU0kVCoVkdNkZtzYvi4f33sRAzvX47U5G7nw4Y/4+3vL2J+bd/IdiBRDKhWR76hCail+eWUT3vvJBVzcuBp/f285Fz78ERNmreNIvsZbpGRRqYgUkTpVUnnyhjZMvKMjtSqV4b6Jn9P98WlMW74t7GgiZ4xKRaSIta1bmVfv6MQTN7Rm76HD3DT6U/qNmcnyzZpWX4o/lYpIFJgZ3VvU5L2fXMADV5xLzpqddBs2lV9Oms+2vd+YPFuk2FCpiERRSlIig7s24OOfXcTNHery8qx1XPjwRzz54QrdaVKKJZWKyBlQOS2Z3/Zoyjv3dKVjgyo8/PZSLvnbx7w2ZwP5GsyXYkSlInIG1U8vy8i+WYwb1IFKaaW4++W5/PAfn/DpSt23RYoHlYpICDo2qMLkIZ155PqWbN1ziF5PZzP4+RxWbdsXdjSR70SlIhKShATjmjYZfPDTC7n3skZ8smIblz7yMb+dvFC3Mpa4pVIRCVmZ5ETuvLghH/3sIq7Lqs3zM1ZzwcMfMnLKSg7laTBf4otKRSRGpJdL4c/XNOetu7vSpm4l/vifxVz6yBTe+HyTZkKWuKFSEYkxjaqX49l+7Rg7oB2pyYkMeekzej41ndlrdp58Y5GQqVREYlSXhum88eMuPNSzOet2HqDnU9MZ8tJnrNuxP+xoIoXS7YR1O2GJA/sO5TFiykqenvIF+flw6/mZDLnobCqUKRV2NCmBdDthkTiXlpLETy5txEf3XkSPVjUZOXUlFz78Ic9+sorDR/LDjifyXyoVkThyVoXS/PW6lvz7rs40rlGe376+iO8/OoV3Fn6pwXyJCSoVkTjUtGYFXhzYnmduzcIMBo+dTe+ns5m/flfY0aSEU6mIxCkz4+Jzq/PW3V35/Q+bsWLLXn7wxDR+8vJcNn51IOx4UkJpoF4D9VJM7D54mKc++oLR01ZhwMAu9bjjwrMpm5IUdjQpZjRQL1IClC9dip93O5cPfnoB3ZqdxZMffsGFD3/Ii5+uIU+D+XKGqFREipmMSqkM692a14acT72qafxy0gKueGwqHy7dosF8iTqVikgx1ap2RSbc1pHhN7UhNy+ffmNm0feZmSzYoMF8iR6VikgxZmZ0a1aDd+65gN90b8L8Dbvo/vg0fjxuDmu2a5p9KXoaqNdAvZQguw4c5ukpkcH8vCPODe3rcNfFDUkvlxJ2NIkjJxqoV6moVKQE2rL7IMPeX874WetISUpgYOd6DOpan3KlNe2LnJxKpRAqFSnpVm3bx1/fWcobn2+icloyQy46m5s61CElKTHsaBLD9JFiETmuelXTePKGNky+83ya1CjP7/+9iIv/+jETZ6/nSH7JfcEpp0+lIiK0yKjICwPb88KA9lROS+an/5zHFcOm8v7izfoYsnwrKhUR+a/ODavyryHn88QNrTmUd4QBz+Vw/YgZzF6zI+xoEidUKiLyNQkJRvcWNXn3Jxfwhx82Y/X2/fR8agYDn8th2eY9YceTGKeBeg3Ui5zQ/tw8xnyymuEffcG+3DyuaZPBPZc2olbFMmFHk5CENlBvZt3MbKmZrTCz+4+zvpKZTTKzz81sppk1K7BuqJktMLOFZnZ3geUtzWyGmc03s9fNrHyBdb8IftdSM/t+NI9NpKRITU5iyEVnM+W+ixjQuR6T523kor9+xB/+vYid+3LDjicxJmqlYmaJwJPA5UAToI+ZNTnmaQ8Ac929BdAXGBZs2wwYBLQDWgLdzaxhsM0o4H53bw5MAn4WbNME6A00BboB/wgyiEgRqJSWzC+vbMKH917IVS1r8swnq+j6lw954oPl7M/NCzuexIhonqm0A1a4+0p3zwXGA1cd85wmwPsA7r4EyDSz6kBjINvd97t7HvAxcHWwzTnAlODxu0DP4PFVwHh3P+Tuq4AVQQYRKUK1Kpbh4eta8tbdXenQoAp/fWcZFzz8EWOz1+jWxhLVUqkFrCvw8/pgWUHzgGsAzKwdUBfIABYAXc2sipmlAlcAtYNtFgA9gsfXFVh+Kr8PMxtsZjlmlrN169bTPDQRaVS9HCP7ZjHxjo7Uq5LGr19bwKWPfMzr8zaSr2tcSqxoloodZ9mxf9MeBCqZ2VzgLmAOkOfui4GHiJyJvEWkfI6eX/cHhpjZbKAccPRN3VP5fbj70+6e5e5Z6enp3+6IROQb2tatzMu3deCZW7NISUrkrnFz6PHkNKYu14u2kiiat4Rbz//OIiByBrKx4BPcfTfQD8DMDFgVfOHuo4HRwbo/Bfs7+jbZZcHyRsCVp/r7RCQ6jt7a+IJG1XhtzgYeeXcZN4+eyflnV+Hn3c6lRUbFsCPKGRLNM5VZQEMzq2dmyUQG0ScXfIKZVQzWAQwEpgRFg5lVC77XIfIW2bhjlicAvwKGB9tPBnqbWYqZ1QMaAjOjeHwicozEBKNn2ww+uPcCft29CYs37aHHE58w5MXPWLl1b9jx5AyI2pmKu+eZ2Z3A20Ai8Iy7LzSz24P1w4kMyD9vZkeARcCAAruYaGZVgMPAEHffGSzvY2ZDgsevAmOC/S00swnBfvKCbY5E6/hEpHApSYkM6FyP67MyGDllJaOmreKthV/S67zaDL2kIdXLlw47okSJLn7UxY8iUbd1zyEe/2A5L326lqREo//59bjtggZUKKOp9uORpr4vhEpF5Mxas30ff3tnGZPnbaRCmVIMuagBfTtmUrqULimLJyqVQqhURMKxYMMu/vL2UqYs20qNCqW553uNuKZNLZISNR1hPND9VEQkpjSrVYHn+7fjpUHtqVa+NPdN/Jxuw6by9sIvNdV+nFOpiEhoOjWoyms/6sRTN7Yh353bxs6m51PT+XTl9rCjyWlSqYhIqMyMy5vX4J27u/Lna5qz4asD9Ho6m35jZrJgw66w48m3pDEVjamIxJQDuUd4dvpqnvpoBbsP5tGxfhUGda3HhY2qkZBwvIkz5EzTQH0hVCoisWvXgcOMn7mWMZ+s5svdB2mQnsaAzvW5pk0tfVosZCqVQqhURGLf4SP5vPH5JkZOXcnCjbupkpbMTR3qcnPHulQtmxJ2vBJJpVIIlYpI/HB3Zqzczqipq/hgyRaSkxLo2aYWAzrX5+xqZcOOV6KcqFSiOaGkiEiRMTM6NahKpwZVWbFlL6OnreLVz9YzbuY6Lj63GgO71KNj/SpE5qaVsOhMRWcqInFr+95DjM1ew9gZa9i+L5emNcszqEt9rmxRg1K6kDJq9PZXIVQqIsXDwcNHmDRnA6OmruSLrfuoUaE0t3bKpHe7OppfLApUKoVQqYgUL/n5zkfLtjByyipmrNxOWnIivc6rQ7/zM6ldOTXseMWGSqUQKhWR4mvBhl2MnrYqcntjdy5vXoOBnevRuk6lsKPFPZVKIVQqIsXfpl0HeHb6al76dC17DuaRVbcSA7vU59Im1UnUxZSnRaVSCJWKSMmx91AeE2at45lPVrF+5wEyq6TSv3M9rm2bQWqyPgj7bahUCqFSESl58o7k8/bCzYycupK5676iYmopbmxfh1s6ZlJNd6Q8JSqVQqhUREoud2f2mp2MnLqSdxZtplRCAj1a1WRgl3qce1b5sOPFNF38KCJyDDMjK7MyWZmVWb1tH2M+WcWEnPW8Mns9XRpWZWCX+nRtWFUXU35LOlPRmYqIBL7an8uLn67luemr2bLnEOdUL8eALvW4qlVNUpI0ieVRevurECoVETmeQ3lHeH3eJkZNXcmSL/eQXi6FWzrW5cb2damUlhx2vNCpVAqhUhGRE3F3pq3Yxsipq5iybCtlSiVybdsMBnSuR2bVtLDjhUalUgiVioicqqVf7mHU1JX8a+5GDufnc2nj6gzqWp+supVK3LiLSqUQKhUR+ba27DnI89PX8MKna/hq/2Fa1q7IoC716Nb0LJJKyCSWKpVCqFRE5HQdyD3CK5+t55lpq1i1bR+1Kpah93m1uTYrgxoVyoQdL6pUKoVQqYjId5Wf77y3eDPPTl/N9C+2k2Bw0TnV6HVebS46t1qxnIJf16mIiERJQoJxWdOzuKzpWazdvp8JOev45+x1vD92C+nlUri2bQa9smqXmIF9nanoTEVEiljekXw+WrqV8bPW8eHSLRzJdzrWr0LvdrX5ftOzKF0qvq950dtfhVCpiEi0bd59kFdmr+flWetYu2M/FcqU4urWteh1Xm0a14jP6WBUKoVQqYjImZKf72Sv3M74Wet4a8GX5B7Jp2VGBXqdV4cerWpSNiV+RiNUKoVQqYhIGHbuy+W1uRsYP3MdSzfvITU5ke4tatDrvDq0qVMx5q97UakUQqUiImFyd+at38X4mWuZPG8j+3OP0LBaWXqdV5tr2mRQOUanhFGpFEKlIiKxYu+hPN74fCPjZ61jztqvSE5M4LKm1el9Xh06NahCQgzdpVKlUgiViojEoqVf7mH8rLVMmrOBr/YfJqNSGXplxc6FlSqVQqhURCSWHTx8hHcWbeblWWv5ZEXsXFipUimESkVE4kXBCys37z4U6oWVKpVCqFREJN7EwoWVoZWKmXUDhgGJwCh3f/CY9ZWAZ4AGwEGgv7svCNYNBQYBBox0978Hy1sBw4HSQB7wI3efaWalgFFAGyLTzzzv7n8+UT6ViojEs7AurAylVMwsEVgGXAqsB2YBfdx9UYHnPAzsdfffmdm5wJPufomZNQPGA+2AXOAt4A53X25m7wCPuvubZnYFcJ+7X2hmNwA93L23maUCi4AL3X11YRlVKiJSHJzpCyvDmlCyHbDC3VcGIcYDVxH5z/6oJsCfAdx9iZllmll1oDGQ7e77g20/Bq4G/gI4cLSCKwAbg8cOpJlZElCGSBntjt7hiYjEhoQEo9PZVel0dtWvXVj5wKT5/OGNRWf0wspolkotYF2Bn9cD7Y95zjzgGmCambUD6gIZwALgj2ZWBTgAXAEcPaW4G3jbzP4KJACdguWvECmtTUAqcI+77zg2lJkNBgYD1KlT57sdoYhIjKmUlky/8+txa6fMr11YOSFn/Rm5sDKan0c7Xh0e+17bg0AlM5sL3AXMAfLcfTHwEPAukbe+5hEZPwG4g0hh1AbuAUYHy9sBR4CaQD3gp2ZW/xsB3J929yx3z0pPT/8OhyciErvMjFa1K/JgzxbM/OX3eKhnc8qWTuIPbyymw5/e549vLDr5Tk5DNM9U1gO1C/ycwf/eqgLA3XcD/QAsck62KvjC3UcTFIaZ/SnYH8AtwNDg8T+JDM4D3AC85e6HgS1m9gmQBaws0qMSEYkzZVOS6HVeHXqdV4elX+7h5VnrqFkxOhdRRvNMZRbQ0MzqmVky0BuYXPAJZlYxWAcwEJgSFA1mVi34XofIW2TjgudtBC4IHl8MLA8erwUutog0oAOwJCpHJiISp845qxy/+UET+p1fLyr7j9qZirvnmdmdwNtEPlL8jLsvNLPbg/XDiQzIP29mR4gM4A8osIuJwZjKYWCIu+8Mlg8ChgUD8gcJxkeAJ4ExRMZjDBjj7p9H6/hEROSbdPGjPlIsIvKtnOgjxeFMHCMiIsWSSkVERIqMSkVERIqMSkVERIqMSkVERIqMSkVERIpMif5IsZltBdaEneMYVYFtYYf4FuIpbzxlhfjKG09ZIb7yxmLWuu5+3HmuSnSpxCIzyyns89+xKJ7yxlNWiK+88ZQV4itvPGUFvf0lIiJFSKUiIiJFRqUSe54OO8C3FE954ykrxFfeeMoK8ZU3nrJqTEVERIqOzlRERKTIqFRERKTIqFRihJnVNrMPzWyxmS00s6En3ypcZpZoZnPM7N9hZzmZ4IZwr5jZkuDPuGPYmQpjZvcEfwcWmNk4MysddqaCzOwZM9tiZgsKLKtsZu+a2fLge6UwMx5VSNaHg78Hn5vZJDOrGGLErzle3gLr7jUzN7OqYWQ7VSqV2JEH/NTdGxO5a+UQM2sScqaTGQosDjvEKRpG5HbT5wItidHcZlYL+DGQ5e7NiNzgrne4qb7hWaDbMcvuB95394bA+8HPseBZvpn1XaCZu7cAlgG/ONOhTuBZvpkXM6sNXErkDrcxTaUSI9x9k7t/FjzeQ+Q/vVrhpiqcmWUAVwKjws5yMmZWHugKjAZw91x3/yrUUCeWBJQJ7m6aSuQW2jHD3acAO45ZfBXwXPD4OeCHZzJTYY6X1d3fcfe84MdsIOOMBytEIX+2AI8C9wEx/8kqlUoMMrNMoDXwachRTuTvRP6S54ec41TUB7YCY4K360aZWVrYoY7H3TcAfyXyinQTsMvd3wk31Smp7u6bIPICCagWcp5T1R94M+wQJ2JmPYAN7j4v7CynQqUSY8ysLDARuNvdd4ed53jMrDuwxd1nh53lFCUBbYCn3L01sI/YeXvma4KxiKuAekBNIM3Mbgo3VfFkZr8k8rbzi2FnKYyZpQK/BH4TdpZTpVKJIWZWikihvOjur4ad5wTOB3qY2WpgPHCxmb0QbqQTWg+sd/ejZ36vECmZWPQ9YJW7b3X3w8CrQKeQM52KzWZWAyD4viXkPCdkZrcA3YEbPbYv1mtA5AXGvODfWwbwmZmdFWqqE1CpxAgzMyLv+S9290fCznMi7v4Ld89w90wig8gfuHvMvpp29y+BdWZ2TrDoEmBRiJFOZC3QwcxSg78TlxCjHyo4xmTgluDxLcC/QsxyQmbWDfg50MPd94ed50Tcfb67V3P3zODf23qgTfB3OiapVGLH+cDNRF71zw2+rgg7VDFyF/CimX0OtAL+FG6c4wvOpl4BPgPmE/k3GlPTdJjZOGAGcI6ZrTezAcCDwKVmtpzIp5QeDDPjUYVkfQIoB7wb/DsbHmrIAgrJG1c0TYuIiBQZnamIiEiRUamIiEiRUamIiEiRUamIiEiRUamIiEiRUamIxBgzyzzeLLUi8UClIiIiRUalIhLDzKx+MAnmeWFnETkVKhWRGBVMKzMR6Ofus8LOI3IqksIOICLHlU5k/qye7r4w7DAip0pnKiKxaRewjsiccCJxQ2cqIrEpl8jdE982s73u/lLIeUROiUpFJEa5+77ghmjvmtk+d4/Z6eRFjtIsxSIiUmQ0piIiIkVGpSIiIkVGpSIiIkVGpSIiIkVGpSIiIkVGpSIiIkVGpSIiIkXm/wOXYelvk+KOMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9996646293695678\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import time\n",
    "\n",
    "k=range(1, 16,2)\n",
    "cv_scores_k = []\t\t#用来放每个模型的结果值\n",
    "for i in k:\n",
    "    start_time = time.time()\n",
    "    print(\"time - {}\".format(start_time))\n",
    "    model= KNeighborsClassifier(n_neighbors=i)\n",
    "    r2_score = cross_val_score(model, X_smotesampled, y_smotesampled,cv=5, scoring='accuracy')\n",
    "    cv_scores_k.append(r2_score.mean())\n",
    "    print(i)\n",
    "    print(\"time - {}\".format(time.time()-start_time))\n",
    "    print(r2_score.mean())\n",
    "    \n",
    "plt.plot(k,cv_scores_k)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('f1')\t\t#通过图像选择最好的参数\n",
    "plt.show()\n",
    "print(max(cv_scores_k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time - 1670848105.5776587\n",
      "16\n",
      "time - 909.881413936615\n",
      "0.998823435668373\n",
      "time - 1670849015.4590726\n",
      "18\n",
      "time - 949.9180860519409\n",
      "0.9987531517298021\n",
      "time - 1670849965.3771586\n",
      "20\n",
      "time - 921.0881395339966\n",
      "0.998690062205811\n",
      "time - 1670850886.4652982\n",
      "22\n",
      "time - 954.7696945667267\n",
      "0.9986136906656471\n",
      "time - 1670851841.2349927\n",
      "24\n",
      "time - 966.7242407798767\n",
      "0.9985478340387465\n",
      "time - 1670852807.9592335\n",
      "26\n",
      "time - 1158.2548341751099\n",
      "0.9984947060230571\n",
      "time - 1670853966.2140677\n",
      "28\n",
      "time - 1259.4531471729279\n",
      "0.9984460053374145\n",
      "time - 1670855225.6672149\n",
      "30\n",
      "time - 1357.4643013477325\n",
      "0.9983945375672384\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.998823435668373\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import time\n",
    "\n",
    "k=range(16, 31,2)\n",
    "cv_scores_k = []\t\t#用来放每个模型的结果值\n",
    "for i in k:\n",
    "    start_time = time.time()\n",
    "    print(\"time - {}\".format(start_time))\n",
    "    model= KNeighborsClassifier(n_neighbors=i)\n",
    "    r2_score = cross_val_score(model, X_smotesampled, y_smotesampled,cv=5, scoring='accuracy')\n",
    "    cv_scores_k.append(r2_score.mean())\n",
    "    print(i)\n",
    "    print(\"time - {}\".format(time.time()-start_time))\n",
    "    print(r2_score.mean())\n",
    "    \n",
    "plt.plot(k,cv_scores_k)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('f1')\t\t#通过图像选择最好的参数\n",
    "plt.show()\n",
    "print(max(cv_scores_k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "k=range(1, 30)\n",
    "cv_scores_k = []\t\t#用来放每个模型的结果值\n",
    "for i in k:\n",
    "    model= KNeighborsClassifier(n_neighbors=i)\n",
    "    r2_score = cross_val_score(model, X_train,y_train,cv=5, scoring='f1')\n",
    "    cv_scores_k.append(r2_score.mean())\n",
    "    \n",
    "    \n",
    "plt.plot(k,cv_scores_k)\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('f1')\t\t#通过图像选择最好的参数\n",
    "plt.show()\n",
    "print(max(cv_scores_k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n",
      "F:\\soft\\Anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  return self._fit(X, y)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最优分类器: {'n_neighbors': 1} 最优分数: 0.9951639375967609\n"
     ]
    }
   ],
   "source": [
    "# X_oversampled, y_oversampled\n",
    "# X_smotesampled, y_smotesampled\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import numpy as np \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "clf_sk = KNeighborsClassifier()\n",
    "\n",
    "param = {\n",
    "    'n_neighbors': np.arange(1, 5, 1),#选取最近的k个点\n",
    "}\n",
    "grid = GridSearchCV(clf_sk, param_grid=param, cv=6,scoring='f1')#scoring='accuracy'\n",
    "grid.fit(X_oversampled, y_oversampled)\n",
    "print('最优分类器:', grid.best_params_, '最优分数:', grid.best_score_)  # 得到最优的参数和分值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>split5_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.704438</td>\n",
       "      <td>0.242684</td>\n",
       "      <td>10.626573</td>\n",
       "      <td>0.242246</td>\n",
       "      <td>1</td>\n",
       "      <td>{'n_neighbors': 1}</td>\n",
       "      <td>0.995228</td>\n",
       "      <td>0.995307</td>\n",
       "      <td>0.994985</td>\n",
       "      <td>0.995235</td>\n",
       "      <td>0.994985</td>\n",
       "      <td>0.995245</td>\n",
       "      <td>0.995164</td>\n",
       "      <td>0.000129</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.517874</td>\n",
       "      <td>0.173487</td>\n",
       "      <td>11.176177</td>\n",
       "      <td>0.412910</td>\n",
       "      <td>2</td>\n",
       "      <td>{'n_neighbors': 2}</td>\n",
       "      <td>0.995231</td>\n",
       "      <td>0.995300</td>\n",
       "      <td>0.994981</td>\n",
       "      <td>0.995208</td>\n",
       "      <td>0.994985</td>\n",
       "      <td>0.995231</td>\n",
       "      <td>0.995156</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.457287</td>\n",
       "      <td>0.131642</td>\n",
       "      <td>10.942648</td>\n",
       "      <td>0.199903</td>\n",
       "      <td>3</td>\n",
       "      <td>{'n_neighbors': 3}</td>\n",
       "      <td>0.991036</td>\n",
       "      <td>0.991209</td>\n",
       "      <td>0.990814</td>\n",
       "      <td>0.991059</td>\n",
       "      <td>0.991023</td>\n",
       "      <td>0.990971</td>\n",
       "      <td>0.991019</td>\n",
       "      <td>0.000117</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.316936</td>\n",
       "      <td>0.115311</td>\n",
       "      <td>11.219304</td>\n",
       "      <td>0.429655</td>\n",
       "      <td>4</td>\n",
       "      <td>{'n_neighbors': 4}</td>\n",
       "      <td>0.991029</td>\n",
       "      <td>0.991176</td>\n",
       "      <td>0.990814</td>\n",
       "      <td>0.991032</td>\n",
       "      <td>0.990996</td>\n",
       "      <td>0.990951</td>\n",
       "      <td>0.991000</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       3.704438      0.242684        10.626573        0.242246   \n",
       "1       3.517874      0.173487        11.176177        0.412910   \n",
       "2       3.457287      0.131642        10.942648        0.199903   \n",
       "3       3.316936      0.115311        11.219304        0.429655   \n",
       "\n",
       "  param_n_neighbors              params  split0_test_score  split1_test_score  \\\n",
       "0                 1  {'n_neighbors': 1}           0.995228           0.995307   \n",
       "1                 2  {'n_neighbors': 2}           0.995231           0.995300   \n",
       "2                 3  {'n_neighbors': 3}           0.991036           0.991209   \n",
       "3                 4  {'n_neighbors': 4}           0.991029           0.991176   \n",
       "\n",
       "   split2_test_score  split3_test_score  split4_test_score  split5_test_score  \\\n",
       "0           0.994985           0.995235           0.994985           0.995245   \n",
       "1           0.994981           0.995208           0.994985           0.995231   \n",
       "2           0.990814           0.991059           0.991023           0.990971   \n",
       "3           0.990814           0.991032           0.990996           0.990951   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.995164        0.000129                1  \n",
       "1         0.995156        0.000126                2  \n",
       "2         0.991019        0.000117                3  \n",
       "3         0.991000        0.000108                4  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grid = pd.DataFrame(grid.cv_results_)\n",
    "df_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'leaf_size': 30,\n",
       " 'metric': 'minkowski',\n",
       " 'metric_params': None,\n",
       " 'n_jobs': None,\n",
       " 'n_neighbors': 3,\n",
       " 'p': 2,\n",
       " 'weights': 'uniform'}"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_sk.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cv': 6,\n",
       " 'error_score': nan,\n",
       " 'estimator__algorithm': 'auto',\n",
       " 'estimator__leaf_size': 30,\n",
       " 'estimator__metric': 'minkowski',\n",
       " 'estimator__metric_params': None,\n",
       " 'estimator__n_jobs': None,\n",
       " 'estimator__n_neighbors': 5,\n",
       " 'estimator__p': 2,\n",
       " 'estimator__weights': 'uniform',\n",
       " 'estimator': KNeighborsClassifier(),\n",
       " 'n_jobs': None,\n",
       " 'param_grid': {'n_neighbors': array([1, 2, 3, 4])},\n",
       " 'pre_dispatch': '2*n_jobs',\n",
       " 'refit': True,\n",
       " 'return_train_score': False,\n",
       " 'scoring': 'f1',\n",
       " 'verbose': 0}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9242167166334675"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9862"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time - 2.2727551460266113\n",
      "1.0\n",
      "time - 0.44010305404663086\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf_sk = KNeighborsClassifier(n_neighbors=1)\n",
    "import time\n",
    "start_time = time.time()\n",
    "# code\n",
    "\n",
    "clf_sk.fit(X_train, y_train_1)\n",
    "print(\"time - {}\".format(time.time()-start_time))\n",
    "print(clf_sk.score(X_train, y_train_1))\n",
    "start_time = time.time()\n",
    "y_pred = clf_sk.predict(X_test)\n",
    "print(\"time - {}\".format(time.time()-start_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9049   70]\n",
      " [  49  832]]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0（预测不被诈骗）</th>\n",
       "      <th>1（预测被诈骗）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0（实际未被诈骗）</th>\n",
       "      <td>9049</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1（实际被诈骗）</th>\n",
       "      <td>49</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           0（预测不被诈骗）  1（预测被诈骗）\n",
       "0（实际未被诈骗）       9049        70\n",
       "1（实际被诈骗）          49       832"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "m = confusion_matrix(y_test, y_pred)  # 传入预测值和真实值\n",
    "print(m)\n",
    "a = pd.DataFrame(m, index=['0（实际未被诈骗）', '1（实际被诈骗）'], columns=['0（预测不被诈骗）', '1（预测被诈骗）'])\n",
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    9098\n",
       "1.0     902\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=pd.DataFrame(y_pred)\n",
    "a.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "查准率： 0.9223946784922394\n",
      "召回率： 0.9443813847900113\n",
      "F1分数： 0.9332585530005608\n",
      "准确率： 0.9881\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"查准率：\",metrics.precision_score(y_test,y_pred))\n",
    "print(\"召回率：\",metrics.recall_score(y_test,y_pred))\n",
    "print(\"F1分数：\",metrics.f1_score(y_test,y_pred))\n",
    "print(\"准确率：\",metrics.accuracy_score(y_test,y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 自动调参吧，试试循环，找到最优的k值\n",
    "best_score = 0.0\n",
    "best_k = -1\n",
    "for k in range(1, 11):\n",
    "    knn_clf = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn_clf.fit(X_train, y_train_1)\n",
    "    score = knn_clf.score(X_test, y_test)\n",
    "    if score > best_score:\n",
    "        best_k = k\n",
    "        best_score = score\n",
    "\n",
    "print(\"best_k = \" + str(best_k))\n",
    "print(\"best_score = \" + str(best_score))"
   ]
  }
 ],
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